Voice encoding method

ABSTRACT

In a voice coding method for adaptively quantizing a difference d n  between an input signal x n  and a predicted value y n  to code the difference, adaptive quantization is performed such that a reversely quantized value q n  of a code L n  corresponding to a section where the absolute value of the difference d n  is small is approximately zero.

TECHNICAL FIELD

The present invention relates generally to a voice coding method, andmore particularly, to improvements of an adaptive pulse code modulation(APCM) method and an adaptive differential pulse code modulation (ADPCM)method.

BACKGROUND

As a coding system of a voice signal, an adaptive pulse code modulation(APCM) method and an adaptive difference pulse code modulation (ADPCM)method, and so on have been known.

The ADPCM is a method of predicting the current input signal from thepast input signal, quantizing a difference between its predicted valueand the current input signal, and then coding the quantized difference.On the other hand, in the ADPCM, a quantization step size is changeddepending on the variation in the level of the input signal.

FIG. 11 illustrates the schematic construction of a conventional ADPCMencoder 4 and a conventional ADPCM decoder 5. n used in the followingdescription is an integer.

Description is now made of the ADPCM encoder 4.

A first adder 41 finds a difference (a prediction error signal d_(n))between a signal x_(n) signal y_(n) on the basis of the followingequation (1):

d_(n)=x_(n)−y_(n)  (1)

A first adaptive quantizer 42 codes the prediction error signal d_(n)found by the first adder 41 on the basis of a quantization step sizeT_(n), to find a code L_(n). That is, the first adaptive quantizer 42finds the code L_(n) on the basis of the following equation (2). Thefound code L_(n) is sent to a memory 6.

L_(n)=[d_(n)/T_(n)]  (2)

In the equation (2), [ ] is Gauss' notation, and represents the maximuminteger which does not exceed a number in the square brackets. Aninitial value of the quantized value T_(n) is a positive number.

A first quantization step size updating device 43 finds a quantizationstep size T_(n+1) corresponding the subsequent voice signal samplingvalue X_(n+1) on the basis of the following equation (3). Therelationship between the code L_(n) and a function M (L_(n)) is as shownin Table 1. Table 1 shows an example in a case where the code L_(n) iscomposed of four bits.

 T_(n+1)=T_(n)×M(L_(n))  (3)

TABLE 1 L_(n) M (L_(n)) 0 −1 0.9 1 −2 0.9 2 −3 0.9 3 −4 0.9 4 −5 1.2 5−6 1.6 6 −7 2.0 7 −8 2.4

A first adaptive reverse quantizer 44 reversely quantizes the predictionerror signal d_(n) using the code L_(n), to find a reversely quantizedvalue q_(n). That is, the first adaptive reverse quantizer 44 finds thereversely quantized value q_(n) on the basis of the following equation(4):

q_(n)=(L_(n)+0.5)×T_(n)  (4)

A second adder 45 finds a reproducing signal w_(n) the basis of thepredicting signal y_(n) ponding to the current voice signal samplingx_(n) and the reversely quantized value q_(n). That is, the second adder45 finds the reproducing signal w_(n) on the basis of the followingequation (5):

w_(n)=y_(n)+q_(n)  (5)

A first predicting device 46 delays the reproducing signal w_(n) by onesampling time, to find a predicting signal y_(n+1) corresponding to thesubsequent voice signal sampling value x₊₁.

Description is now made of the ADPCM decoder 5.

A second adaptive reverse quantizer 51 uses a code L_(n)′ obtained fromthe memory 6 and a quantization step size T_(n)′ obtained by a secondquantization step size updating device 52, to find a reversely quantizedvalue q_(n)′ on the basis of the following equation (6).

q_(n)′=(L_(n)′+0.5)×T_(n)′  (6)

If L_(n) found in the ADPCM encoder 4 is correctly transmitted to theADPCM decoder 5, that is, L_(n)=L_(n)′, the values of q_(n)′, y_(n)′,T_(n)′ and w_(n)′ used on the side of the ADPCM decoder 5 arerespectively equal to the values of q_(n), y_(n), T_(n) and w_(n) usedon the side of the ADPCM encoder 4.

The second quantization step size updating device 52 uses the codeL_(n)′ obtained from the memory 6, to find a quantization step sizeT_(n+1)′ used with respect to the subsequent code L_(n+1)′ on the basisof the following equation (7) The relationship between L_(n)′ and afunction M (L_(n)′) in the following equation (7) is the same as therelationship between L_(n) and the function M (L_(n)) in the foregoingTable 1.

T_(n+1)′=T_(n)′×M(L_(n)′)  (7)

A third adder 53 finds a reproducing signal w_(n)′ on the basis of apredicting signal y_(n)′ obtained by a second predicting device 54 andthe reversely quantized value q_(n)′. That is, the third adder 53 findsthe reproducing signal w_(n)′ on the basis of the following equation(8). The found reproducing signal w_(n)′ is outputted from the ADPCMdecoder 5.

w_(n)′=y_(n)′+q_(n)′  (8)

The second predicting device 54 delays the reproducing signal w_(n)′ byone sampling time, to find the subsequent predicting signal y_(n+1)′,and sends the predicting signal y_(n+1)′ to the third adder 53.

FIGS. 12 and 13 illustrate the relationship between the reverselyquantized value q_(n) and the prediction error signal d_(n) in a casewhere the code L_(n) is composed of three bits.

T in FIG. 12 and U in FIG. 13 respectively represent quantization stepsizes determined by the first quantization step size updating device 43at different time points, where it is assumed that T<U.

In a case where the range A to B of the prediction error signal d_(n) isindicated by A and B, the range is indicated by “[A” when a boundary Ais included in the range, while being indicated by “(A” when it is notincluded therein. Similarly, the range is indicated by “B]” when aboundary B is included in the range, while being indicated by “B)” whenit is not included therein.

In FIG. 12, the reversely quantized value q_(n) is 0.5T when the valueof the prediction error signal d_(n) is in the range of [0, T), 1.5Twhen it is in the range of [T, 2T), 2.5T when it is in the range of [2T,3T) and 3.5T when it is in the range of [3T, ∞].

The reversely quantized value q_(n) is −0.5T when the value of theprediction error signal d_(n) is in the range of [−T, 0), −1.5T when itis in the range of [−2T, −T) −2.5 when it is in the range of [−3T, −2T),and −3.5T when it is in the range of [−∞, −3T)

In the relationship between the reversely quantized value q_(n) and theprediction error signal d_(n) in FIG. 13, T in FIG. 12 is replaced withU. As shown in FIGS. 12 and 13, the relationship between the reverselyquantized value q_(n) and the prediction error signal d_(n) is sodetermined that the characteristics are symmetrical in a positive rangeand a negative range of the prediction error signal d_(n) in the priorart. As a result, even when the prediction error signal d_(n) is small,the reversely quantized value q_(n) is not zero.

As can be seen from the equation (3) and Table 1, when the code L_(n)becomes large, the quantization step size T_(n) is made large. That is,the quantization step size is made small as shown in FIG. 12 when theprediction error signal d_(n) is small, while being made large as shownin FIG. 13 when the prediction error signal d_(n) is large.

In a voice signal, there exist a lot of silent sections where theprediction error signal d_(n) is zero. In the above-mentioned prior art,however, even when the prediction error signal d_(n) is zero, thereversely quantized value q_(n) is 0.5T(or 0.5U) which is not zero, sothat an quantizing error is increased.

In the above-mentioned prior art, even if the absolute value of theprediction error signal d_(n) is rapidly changed from a large value to asmall value, a large value corresponding to the previous predictionerror signal d_(n) whose absolute value is large is maintained as thequantization step size, so that the quantizing error is increased. Thatis, in a case where the quantization step size is a relatively largevalue U as shown in FIG. 13, even if the absolute value of theprediction error signal d_(n) is rapidly decreased to a value close tozero, the reversely quantized value q_(n) is 0.5U which is a largevalue, so that the quantizing error is increased.

Furthermore, even if the absolute value of the prediction error signald_(n) is rapidly changed from a small value to a large value, a smallvalue corresponding to the previous prediction error signal d_(n) whoseabsolute value is small is maintained as the quantization step size, sothat the quantizing error is increased.

Such a problem similarly occurs even in APCM using an input signal as itis in place of the prediction error signal d_(n).

An object of the present invention is to provide a voice coding methodcapable of decreasing a quantizing error when a prediction error signald_(n) is zero or an input signal is rapidly changed.

DISCLOSURE OF THE INVENTION

A first voice coding method according to the present invention is avoice coding method for adaptively quantizing a difference d_(n) betweenan input signal x_(n) and a predicted value y_(n) to code thedifference, characterized in that adaptive quantization is performedsuch that a reversely quantized value q_(n) of a code L_(n)corresponding to a section where the absolute value of the differenced_(n) is small is approximately zero.

A second voice coding method according to the present invention ischaracterized by comprising the first step of adding, when a firstprediction error signal d_(n) which is a difference between an inputsignal x_(n) and a predicted value y_(n) corresponding to the inputsignal x_(n) is not less than zero, one-half of a quantization step sizeT_(n) to the first prediction error signal d_(n) to produce a secondprediction error signal e_(n), while subtracting, when the firstprediction error signal dais less than zero, one-half of thequantization step size T_(n) from the first prediction error signald_(n) to produce a second prediction error signal e_(n), the second stepof finding a code L_(n) on the basis of the second prediction errorsignal e_(n) found in the first step and the quantization step sizeT_(n), the third step of finding a reversely quantized value q_(n) onthe basis of the code L_(n) found in the second step, the fourth step offinding a quantization step size T_(n+1) corresponding to the subsequentinput signal x_(n+1) on the basis of the code L_(n) found in the secondstep, and the fifth step of finding a predicted value y_(n+1)corresponding to the subsequent input signal x_(n+1) on the basis of thereversely quantized value q_(n) found in the third step and thepredicted value y_(n).

In the second step, the code L_(n) is found on the basis of thefollowing equation (9), for example:

L_(n)=[e_(n)/T_(n)]  (9)

where [ ] is Gauss' notation, and represents the maximum integer whichdoes not exceed a number in the square brackets.

In the third step, the reversely quantized value q_(n) is found on thebasis of the following equation (10), for example:

q_(n)=L_(n)×T_(n)  (10)

In the fourth step, the quantization step size T_(n+1) is found on thebasis of the following equation (11), for example:

T_(n+1)=T_(n)×M(L_(n))  (11)

where M (L_(n)) is a value determined depending on L_(n).

In the fifth step, the predicted value y_(n+1) is found on the basis ofthe following equation (12), for example:

y_(n+1)=y_(n)+q_(n)  (12)

A third voice coding method according to the present invention is avoice coding method for adaptively quantizing a difference d_(n) betweenan input signal x_(n) and a predicted value y_(n) to code thedifference, characterized in that adaptive quantization is performedsuch that a reversely quantized value q_(n) of a code L_(n)corresponding to a section where the absolute value of the differenced_(n) is small is approximately zero, and a quantization step sizecorresponding to a section where the absolute value of the differenced_(n) is large is larger, as compared with that corresponding to thesection where the absolute value of the difference d_(n) is small.

A fourth voice coding method according to the present invention ischaracterized by comprising the first step of adding, when a firstprediction error signal d_(n) which is a difference between an inputsignal x_(n) and a predicted value y_(n) corresponding to the inputsignal x_(n) is not less than zero, one-half of a quantization step sizeT_(n) to the first prediction error signal d_(n) to produce a secondprediction error signal e_(n), while subtracting, when the firstprediction error signal d_(n) is less than zero, one-half of thequantization step size T_(n) from the first prediction error signald_(n) to produce a second prediction error signal e_(n), the second stepof finding, on the basis of the second prediction error signal e_(n)found in the first step and a table previously storing the relationshipbetween the second prediction error signal e_(n) and a code L_(n), thecode L_(n), the third step of finding, on the basis of the code L_(n)found in the second step and a table previously storing the relationshipbetween the code L_(n) and a reversely quantized value q_(n), thereversely quantized value q_(n), the fourth step of finding, on thebasis of the code L_(n) found in the second step and a table previouslystoring the relationship between the code L_(n) and a quantization stepsize T_(n+1) corresponding to the subsequent input signal x_(n+1), thequantization step size T_(n+1) corresponding to the subsequent inputsignal x_(n+1), and the fifth step of finding a predicted value y_(n+1)corresponding to the subsequent input signal x_(n+1) on the basis of thereversely quantized value q_(n) found in the third step and thepredicted value y_(n), wherein each of the tables is produced so as tosatisfy the following conditions (a), (b) and (c):

(a) The quantization step size T_(n) is so changed as to be increasedwhen the absolute value of the difference d_(n) is so changed as to beincreased,

(b) The reversely quantized value q_(n) of the code L_(n) correspondingto a section where the absolute value of the difference d_(n) is smallis approximately zero, and

(c) A substantial quantization step size corresponding to a sectionwhere the absolute value of the difference d_(n) is large is larger, ascompared with that corresponding to the section where the a absolutevalue of the difference d_(n) is small.

In the fifth step, the predicted value y_(n+1) is found on the basis ofthe following equation (13), for example:

y_(n+1)=y_(n)+q_(n)  (13)

A fifth voice coding method according to the present invention is avoice coding method for adaptively quantizing an input signal x_(n) tocode the input signal, characterized in that adaptive quantization isperformed such that a reversely quantized value of a code L_(n)corresponding to a section where the absolute value of the input signalx_(n) is small is approximately zero.

A sixth voice coding method according to the present invention ischaracterized by comprising the first step of adding one-half of aquantization step size T_(n) to an input signal x_(n) to produce acorrected input signal g_(n) when the input signal x_(n) is not lessthan zero, while subtracting one-half of the quantization step sizeT_(n) from the input signal x_(n) to produce a corrected input signalg_(n) when the input signal x_(n) is less than zero, the second step offinding a code L_(n) on the basis of the corrected input signal g_(n)found in the first step and the quantization step size T_(n), the thirdstep of finding a quantization step size T_(n+1) corresponding to thesubsequent input signal x_(n+1) on the basis of the code L_(n) found inthe second step, and the fourth step of finding a reproducing signalw_(n)′ on the basis of the code L_(n)′(=L_(n)) found in the second step.

In the second step, the code L_(n) is found on the basis of thefollowing equation (14), for example:

L_(n)=[g_(n)/T_(n)]  (14)

where [ ] is Gauss' notation, and represents the maximum integer whichdoes not exceed a number in the square brackets.

In the third step, the quantization step size T_(n+1) is found on thebasis of the following equation (15), for example:

T_(n+1)=T_(n)×M(L_(n))  (15)

where M (L_(n)) is a value determined depending on L_(n).

In the fourth step, the reproducing signal w_(n)′ is found on the basisof the following equation (16), for example:

w_(n)′=L_(n)′(=L_(n))×T_(n)′  (16)

A seventh voice coding method according to the present invention is avoice coding method for adaptively quantizing an input signal x_(n) tocode the input signal, characterized in that adaptive quantization isperformed such that a reversely quantized value q_(n) of a code L_(n)corresponding to a section where the absolute value of the input signalx_(n) is small is approximately zero, and a quantization step sizecorresponding to a section where the absolute value of the input signalx_(n) is large is larger, as compared with that corresponding to thesection where the absolute value of the input signal x_(n) is small.

An eighth voice coding method according to the present invention ischaracterized by comprising the first step of adding one-half of aquantization step size T_(n) to an input signal x_(n) to produce acorrected input signal g_(n) when the input signal d_(n) is not lessthan zero, while subtracting one-half of the quantization step sizeT_(n) from the input signal x_(n) to produce a corrected input signalg_(n) when the input signal x_(n) is less than zero, the second step offinding, on the basis of the corrected input signal g_(n) found in thefirst step and a table previously storing the relationship between thesignal g_(n) and a code L_(n), the code L_(n), the third step offinding, on the basis of the code L_(n) found in the second step and atable previously storing the relationship between the code L_(n) and aquantization step size T_(n+1) corresponding to the subsequent inputsignal x_(n+1), the quantization step size T_(n+1) corresponding to thesubsequent input signal x_(n+1), and the fourth step of finding, on thebasis of the code L_(n)′(=L_(n)) found in the second step and a tablestoring the relationship between the code L_(n)′(=L_(n)) and areproducing signal w_(n)′, the reproducing signal w_(n)′, wherein eachof the tables is produced so as to satisfy the following conditions (a),(b) and (c):

(a) The quantized value T_(n) is so changed as to be increased when theabsolute value of the input signal x_(n) is so changed as to beincreased,

(b) The reversely quantized value q_(n) of the code L_(n) correspondingto a section where the absolute value of the input signal x_(n) is smallis approximately zero, and

(c) A substantial quantization step size corresponding to a sectionwhere the absolute value of the input signal x_(n) is large is madelarger, as compared with that corresponding to the section where theabsolute value of the input signal x_(n) is small.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a first embodiment of the presentinvention;

FIG. 2 is a flow chart showing operations performed by an ADPCM encodershown in FIG. 1;

FIG. 3 is a flow chart showing operations performed by an ADPCM decodershown in FIG. 1;

FIG. 4 is a graph showing the relationship between a prediction errorsignal d_(n) and a reversely quantized value q_(n);

FIG. 5 is a graph showing the relationship between a prediction errorsignal d_(n) and a reversely quantized value q_(n);

FIG. 6 is a block diagram showing a second embodiment of the presentinvention;

FIG. 7 is a flow chart showing operations performed by an ADPCM encodershown in FIG. 6;

FIG. 8 is a flow chart showing operations performed by an ADPCM decodershown in FIG. 6;

FIG. 9 is a graph showing the relationship between a prediction errorsignal d_(n) and a reversely quantized value q_(n);

FIG. 10 is a block diagram showing a third embodiment of the presentinvention;

FIG. 11 is a block diagram showing a conventional example;

FIG. 12 is a graph showing the relationship between a prediction errorsignal d_(n) and a reversely quantized value q_(n) in the conventionalexample; and

FIG. 13 is a graph showing the relationship between a prediction errorsignal d_(n) and a reversely quantized value q_(n) in the conventionalexample.

BEST MODE FOR CARRYING OUT THE INVENTION [1] Description of FirstEmbodiment

Referring now to FIGS. 1 to 5, a first embodiment of the presentinvention will be described.

FIG. 1 illustrates the schematic construction of an ADPCM encoder 1 andan ADPCM decoder 2. n used in the following description is an integer.

Description is now made of the ADPCM encoder 1. A first adder 11 finds adifference (hereinafter referred to as a first prediction error signald_(n)) between a signal x_(n) inputted to the ADPCM encoder 1 and apredicting signal y_(n) on the basis of the following equation (17):

d_(n)=x_(n)−y_(n)  (17)

A signal generator 19 generates a correcting signal a_(n) on the basisof the first prediction error signal d_(n) and a quantization step sizeT_(n) obtained by a first quantization step size updating device 18.That is, the signal generator 19 generates the correcting signal a_(n)on the basis of the following equation (18):

in the case of d_(n)≧0: a_(n)=T_(n)/2

in the case of d_(n)<0: a_(n)=−T_(n)/2  (18)

A second adder 12 finds a second prediction error signal e_(n) on thebasis of the first prediction error signal d_(n) and the correctingsignal a_(n) obtained by the signal generator 19. That is, the secondadder 12 finds the second prediction error signal e_(n) on the basis ofthe following equation (19):

e_(n)=d_(n)+a_(n)  (19)

Consequently, the second prediction error signal e_(n) is expressed bythe following equation (20):

in the case of d_(n)≧0: e_(n)=d_(n)+T_(n)/2

in the case of d_(n)<0: e_(n)=d_(n)−T_(n)/2   (20)

A first adaptive quantizer 14 codes the second prediction error signale_(n) found by the second adder 12 on the basis of the quantization stepsize T_(n) obtained by the first quantization step size updating device18, to find a code L_(n). That is, the first adaptive quantizer 14 findsthe code L_(n) on the basis of the following equation (21). The foundcode L_(n) is sent to a memory 3.

L_(n)=[e_(n)/T_(n)]  (21)

In the equation (21), [ ] is Gauss' notation, and represents the maximuminteger which does not exceed a number in the square brackets. Aninitial value of the quantization step size T_(n) is a positive number.

The first quantization step size updating device 18 finds a quantizationstep size T_(n+1) corresponding the subsequent voice signal samplingvalue X_(n+1) on the basis of the following equation (22). Therelationship between the code L_(n) and a function M (L_(n)) is the sameas the relationship between the code L_(n) and the function M (L_(n)) inthe foregoing Table 1.

T_(n+1)=T_(n)×M(L_(n))  (22)

A first adaptive reverse quantizer 15 find a reversely quantized valueq_(n) on the basis of the following equation (23).

q_(n)=L_(n)×T_(n)  (23)

A third adder 16 finds a reproducing signal w_(n) on the basis of thepredicting signal y_(n) corresponding to the current voice signalsampling value x_(n) and the reversely quantized value q_(n). That is,the third adder 16 finds the reproducing signal w_(n) on the basis ofthe following equation (24):

w_(n)=y_(n)+q_(n)  (24)

A first predicting device 17 delays the reproducing signal w_(n) by onesampling time, to find a predicting signal y_(n+1) corresponding to thesubsequent voice signal sampling value x_(n+1).

Description is now made of the ADPCM decoder 2.

A second adaptive reverse quantizer 22 uses a code L_(n)′ obtained fromthe memory 3 and a quantization step size T_(n)′ obtained by a secondquantization step size updating device 23, to find a reversely quantizedvalue q_(n)′ on the basis of the following equation (25).

 q_(n)′L_(n)′×T_(n)′  (25)

If L_(n) found in the ADPCM encoder 1 is correctly transmitted to theADPCM decoder 2, that is, L_(n)=L_(n)′, the values of q_(n)′, y_(n)′,T_(n)′ and w_(n)′ used on the side of the ADPCM decoder 2 arerespectively equal to the values of q_(n), y_(n), T_(n) and w_(n) usedon the side of the ADPCM encoder 1.

The second quantization step size updating device 23 uses the codeL_(n)′ obtained from the memory 3, to find a quantization step sizeT_(n+1)′ used with respect to the subsequent code L_(n+1)′ on the basisof the following equation (26). The relationship between the code L_(n)′and a function M (L_(n)′) is the same as the relationship between thecode L_(n) and the function M (L_(n)) in the foregoing Table 1.

T_(n+1)′=T_(n)′×M(L_(n)′)  (26)

A fourth adder 24 finds a reproducing signal w_(n)′ on the basis of apredicting signal y_(n)′ obtained by a second predicting device 25 andthe reversely quantized value q_(n)′. That is, the fourth adder 24 findsthe reproducing signal w_(n)′ on the basis of the following equation(27). The found reproducing signal w_(n)′ is outputted from the ADPCMdecoder 2.

w_(n)′=y_(n)′+q_(n)′  (27)

The second predicting device 25 delays the reproducing signal w_(n)′ byone sampling time, to find the subsequent predicting signal y_(n+1)′,and sends the predicting signal y_(n+1)′ to the fourth adder 24.

FIG. 2 shows the procedure for operations performed by the ADPCM encoder1.

The predicting signal y_(n) is first subtracted from the input signalx_(n), to find the first prediction error signal d_(n) (step 1).

It is then judged whether the first prediction error signal d_(n) is notless than zero or less than zero (step 2). When the first predictionerror signal d_(n) is not less than zero, one-half of the quantizationstep size T_(n) is added to the first prediction error signal d_(n), tofind the second prediction error signal e_(n) (step 3).

When the first prediction error signal d_(n) is less than zero, one-halfof the quantization step size T_(n) is subtracted from the firstprediction error signal d_(n), to find the second prediction errorsignal e_(n) (step 4).

When the second prediction error signal e_(n) is found in the step 3 orthe step 4, coding based on the foregoing equation (21) and reversequantization based on the foregoing equation (23) are performed (step5). That is, the code L_(n) and the reversely quantized value q_(n) arefound.

The quantization step size T_(n) is then updated on the basis of theforegoing equation (22) (step 6). The predicting signal y_(n+1)corresponding to the subsequent voice signal sampling value x_(n+1) isfound on the basis of the foregoing equation (24) (step 7).

FIG. 3 shows the procedure for operations performed by the ADPCM decoder2.

The code L_(n)′ is first read out from the memory 3, to find thereversely quantized value q_(n)′ on the basis of the foregoing equation(25) (step 11).

Thereafter, the subsequent predicting signal Y_(n+1)′ is found on thebasis of the foregoing equation (27) (step 12).

The quantization step size T_(n+1)′ used with respect to the subsequentcode L_(n+1)′ is found on the basis of the foregoing equation (26) (step13).

FIGS. 4 and 5 illustrate the relationship between the reverselyquantized value q_(n) obtained by the first adaptive reverse quantizer15 in the ADPCM encoder 1 and the first prediction error signal d_(n) ina case where the code L_(n) is composed of three bits.

T in FIG. 4 and U in FIG. 5 respectively represent quantization stepsizes determined by the first quantization step size updating device 18at different time points, where it is assumed that T<U.

In a case where the range A to B of the first prediction error signald_(n) is indicated by A and B, the range is indicated by “[A” when aboundary A is included in the range, while being indicated by “(A” whenit is not included therein. Similarly, the range is indicated by “B]”when a boundary B is included in the range, while being indicated by“B)” when it is not included therein.

In FIG. 4, the reversely quantized value q_(n) is n zero when the valueof the first prediction error signal d_(n) is in the range of (−0.5T,0.5T) T when it is in the range of [0.5T, 1.5T), 2T when it is in therange of [1.5T, 2.5T), and 3T when it is in the range of [2.5T, ∞].

Furthermore, the reversely quantized value q_(n) is −T when the value ofthe first prediction error signal d_(n) is in the range of (−1.5T,−0.5T], −2T when it is in the range of (−2.5T, −1.5T], −3T when it is inthe range of (−3.5T, −2.5T], and −4T when it is in the range of [∞,−3.5T].

In the relationship between the reversely quantized value q_(n) and thefirst prediction error signal d_(n) in FIG. 5, T in FIG. 4 is replacedwith U.

Also in the first embodiment, when the code L_(n) becomes large, thequantization step size T_(n) is made large, as can be seen from theforegoing equation (22) and Table 1. That is, the quantization step sizeis made small as shown in FIG. 4 when the prediction error signal d_(n)is small, while being made large as shown in FIG. 5 when it is large.

According to the first embodiment, when the prediction error signald_(n) which is a difference between the input signal x_(n) and thepredicting signal y_(n) is zero, the reversely quantized value q_(n) iszero. When the prediction error signal d_(n) is zero as in a silentsection of a voice signal, therefore, a quantizing error is decreased.

When the absolute value of the first prediction error signal d_(n) israpidly changed from a large value to a small value, a large valuecorresponding to the previous prediction error signal d_(n) whoseabsolute value is large is maintained as the quantization step size.However, the reversely quantized value q_(n) can be made zero, so thatthe quantizing error is decreased. That is, in a case where thequantization step size is a relatively large value U as shown in FIG. 5,when the absolute value of the prediction error signal d_(n) is rapidlydecreased to a value close to zero, the reversely quantized value q_(n)is zero, so that the quantizing error is decreased.

[2] Description of Second Embodiment

Referring now to FIGS. 6 to 9, a second embodiment of the presentinvention will be described.

FIG. 6 illustrates the schematic construction of an ADPCM encoder 101and an ADPCM decoder 102. n used in the following description is aninteger.

Description is now made of the ADPCM encoder 101.

The ADPCM encoder 101 comprises first storage means 113. The firststorage means 113 stores a translation table as shown in Table 2. Table2 shows an example in a case where a code L_(n) is composed of fourbits.

TABLE 2 Second Prediction Quantization Error Signal e_(n) L_(n) q_(n)Step Size T_(n+1) 11T_(n) ≦ e_(n) 0111 12T_(n) T_(n+1) = T_(n) × 2.58T_(n) ≦ e_(n) < 11T_(n) 0110 9T_(n) T_(n+1) = T_(n) × 2.0 6T_(n) ≦e_(n) < 8T_(n) 0101 6.5T_(n) T_(n+1) = T_(n) × 1.25 4T_(n) ≦ e_(n) <6T_(n) 0100 4.5T_(n) T_(n+1) = T_(n) × 1.0 3T_(n) ≦ e_(n) < 4T_(n) 00113T_(n) T_(n+1) = T_(n) × 1.0 2T_(n) ≦ e_(n) < 3T_(n) 0010 2T_(n) T_(n+1)= T_(n) × 1.0 T_(n) ≦ e_(n) < 2T_(n) 0001 T_(n) T_(n+1) = T_(n) × 0.75−T_(n) < e_(n) < T_(n) 0000 0 T_(n+1) = T_(n) × 0.75 −2T_(n) < e_(n) ≦−T_(n) 1111 −T_(n) T_(n+1) = T_(n) × 0.75 −3T_(n) < e_(n) ≦ −2T_(n) 1110−2T_(n) T_(n+1) = T_(n) × 1.0 −4T_(n) < e_(n) ≦ −3T_(n) 1101 −3T_(n)T_(n+1) = T_(n) × 1.0 −5T_(n) < e_(n) ≦ −4T_(n) 1100 −4T_(n) T_(n+1) =T_(n) × 1.0 −7T_(n) < e_(n) ≦ −5T_(n) 1011 −5.5T_(n) T_(n+1) = T_(n) ×1.25 −9T_(n) < e_(n) ≦ −7T_(n) 1010 −7.5T_(n) T_(n+1) = T_(n) × 2.0−12T_(n) < e_(n) ≦ −9T_(n) 1001 −10T_(n) T_(n+1) = T_(n) × 2.5 e_(n) ≦−12T_(n) 1000 −13T_(n) T_(n+1) = T_(n) × 5.0

The translation table comprises the first column storing the range of asecond prediction error signal e_(n), the second column storing a codeL_(n) corresponding to the range of the second prediction error signale_(n) in the first column, the third column storing a reverselyquantized value q_(n) corresponding to the code L_(n) in the secondcolumn, and the fourth column storing a calculating equation of aquantization step size T_(n+1) corresponding to the code L_(n) in thesecond column. The quantization step size is a value for determining asubstantial quantization step size, and is not the substantialquantization step size itself.

In the second embodiment, conversion from the second prediction errorsignal e_(n) to the code L_(n) in a first adaptive quantizer 114,conversion from the code L_(n) to the reversely quantized value q_(n) ina first adaptive reverse quantizer 115, and updating of a quantizationstep size T_(n) in a first quantization step size updating device 118are performed on the basis of the translation table stored in the firststorage means 113.

A first adder 111 finds a difference (hereinafter referred to as a firstprediction error signal d_(n)) between a signal x_(n) inputted to theADPCM encoder 101 and a predicting signal y_(n) on the basis of thefollowing equation (28):

d_(n)=x_(n)−y_(n)  (28)

A signal generator 119 generates a correcting signal a_(n) on the basisof the first prediction error signal d_(n) and the quantization stepsize T_(n) obtained by a first quantization step size updating device118. That is, the signal generator 119 generates a correcting signala_(n) on the basis of the following equation (29):

in the case of d_(n)≧0: a_(n)=T_(n)/2

in the case of d_(n)<0: a_(n)=−T_(n)/2  (29)

A second adder 112 finds a second prediction error signal e_(n) on thebasis of the first prediction error signal d_(n) and the correctingsignal a_(n) obtained by the signal generator 119. That is, the secondadder 112 finds the second prediction error signal e_(n) on the basis ofthe following equation (30):

e_(n)=d_(n)+a_(n)  (30)

Consequently, the second prediction error signal e_(n) is expressed bythe following equation (31):

in the case of d_(n)≧0: e_(n)=d_(n)+T_(n)/2

in the case of d_(n)<0: e_(n)=d_(n)−T_(n)/2  (31)

The first adaptive quantizer 114 finds a code L_(n) on the basis of thesecond prediction error signal e_(n) found by the second adder 112 andthe translation table. That is, the code L_(n) corresponding to thesecond prediction error signal e_(n) out of the respective codes L_(n)in the second column of the translation table is read out from the firststorage means 113 and is outputted from the first adaptive quantizer114. The found code L_(n) is sent to a memory 103.

The first adaptive reverse quantizer 115 finds the reversely quantizedvalue q_(n) on the basis of the code L_(n) found by the first adaptivequantizer 114 and the translation table. That is, the reverselyquantized value q_(n) corresponding to the code L_(n) found by the firstadaptive quantizer 114 is read out from the first storage means 113 andis outputted from the first adaptive reverse quantizer 115.

The first quantization step size updating device 118 finds thesubsequent quantization step size T_(n+1) on the basis of the code L_(n)found by the first adaptive quantizer 114, the current quantization stepsize T_(n), and the translation table. That is, the subsequentquantization step size T_(n+1) is found on the basis of the quantizationstep size calculating equation corresponding to the code L_(n) found bythe first adaptive quantizer 114 out of the quantization step sizecalculating equations in the fourth column of the translation table.

A third adder 116 finds a reproducing signal w_(n) on the basis of thepredicting signal y_(n) corresponding to the current voice signalsampling value x_(n) and the reversely quantized value q_(n). That is,the third adder 116 finds the reproducing signal w_(n) on the basis ofthe following equation (32):

w_(n)=y_(n)+q_(n)  (32)

A first predicting device 117 delays the reproducing signal w_(n) by onesampling time, to find a predicting signal y_(n+1) corresponding to thesubsequent voice signal sampling value x_(n+1).

Description is now made of the ADPCM decoder 102.

The ADPCM decoder 102 comprises second storage means 121. The secondstorage means 121 stores a translation table having the same contents asthose of the translation table stored in the first storage means 113.

A second adaptive reverse quantizer 122 finds a reversely quantizedvalue q_(n)′ on the basis of a code L_(n)′ obtained from the memory 103and the translation table. That is, a reversely quantized value q_(n)′corresponding to the code L_(n) in the second column which correspondsto the code L_(n)′ obtained from the memory 103 out of the reverselyquantized values q_(n) in the third column of the translation table isread out from the second storage means 121 and is outputted from thesecond adaptive reverse quantizer 122.

If L_(n) found in the ADPCM encoder 101 is correctly transmitted to theADPCM decoder 2, that is, L_(n)=L_(n)′, the values of q_(n)′, y_(n)′,T_(n)′ and w_(n)′ used on the side of the ADPCM decoder 102 arerespectively equal to the values of q_(n), y_(n), T_(n) and w_(n) usedon the side of the ADPCM encoder 101.

A second quantization step size updating device 123 finds the subsequentquantization step size T_(n+1)′ on the basis of the code L_(n)′ obtainedfrom the memory 103, the current quantization step size T_(n)′ and thetranslation table. That is, the subsequent quantization step sizeT_(n+1)′ is found on the basis of the quantization step size calculatingequation corresponding to the code L_(n)′ obtained from the memory 103out of the quantization step size calculating equations in the fourthcolumn of the translation table.

A fourth adder 124 finds a reproducing signal w_(n)′ on the basis of apredicting signal y_(n)′ obtained by a second predicting device 125 andthe reversely quantized value q_(n)′. That is, the fourth adder 124finds the reproducing signal w_(n)′ on the basis of the followingequation (33). The found reproducing signal w_(n)′ is outputted from theADPCM decoder 102.

w_(n)′=y_(n)′+q_(n)′  (33)

The second predicting device 125 delays the reproducing signal w_(n)′ byone sampling time, to find the subsequent predicting signal y_(n+1)′,and sends the predicting signal y_(n+1)′ to the fourth adder 124.

FIG. 7 shows the procedure for operations performed by the ADPCM encoder101.

The predicting signal y_(n) is first subtracted from the input signalx_(n), to find the first prediction error signal d_(n) (step 21).

It is then judged whether the first prediction error signal d_(n) is notless than zero or less than zero (step 22). When the first predictionerror signal d_(n) is not less than zero, one-half of the quantizationstep size T_(n) is added to the first prediction error signal d_(n), tofind the second prediction error signal e_(n) (step 23).

When the first prediction error signal d_(n) is less than zero, one-halfof the quantization step size T_(n) is subtracted from the firstprediction error signal d_(n), to find the second prediction errorsignal e_(n) (step 24).

When the second prediction error signal e_(n) is found in the step 23 orthe step 24, coding and reverse quantization are performed on the basisof the translation table (step 25). That is, the code L_(n) and thereversely quantized value q_(n) are found.

The quantization step size T_(n) is then updated on the basis of thetranslation table (step 26). The predicting signal y_(n+1) correspondingto the subsequent voice signal sampling value x_(n+1) is found on thebasis of the foregoing equation (32) (step 27).

FIG. 8 shows the procedure for operations performed by the ADPCM decoder102.

The code L_(n)′ is first read out from the memory 103, to find thereversely quantized value q_(n)′ on the basis of the translation table(step 31).

Thereafter, the subsequent predicting signal y_(n+1)′ is found on thebasis of the foregoing equation (33) (step 32).

The quantization step size T_(n+1)′ used with respect to the subsequentcode L_(n+1)′ is found on the basis of the translation table (step 33).

FIG. 9 illustrates the relationship between the reversely quantizedvalue q_(n) obtained by the first adaptive reverse quantizer 115 in theADPCM encoder 101 and the first prediction error signal d_(n) in a casewhere the code L_(n) is composed of four bits. T represents aquantization step size determined by the first quantization step sizeupdating device 118 at a certain time point.

In a case where the range A to B of the first prediction error signald_(n) is indicated by A and B, the range is indicated by “[A” when aboundary A is included in the range, while being indicated by “(A” whenit is not included therein. Similarly, the range is indicated by “B]”when a boundary B is included in the range, while being indicated by“B)” when it is not included therein.

The reversely quantized value q_(n) is zero when the value of the firstprediction error signal d_(n) is in the range of (−0.5T, 0.5T), T whenit is in the range of [0.5T, 1.5T), 2T when it is in the range of [1.5T,2.5T), and 3T when it is in the range of [2.5T, 3.5T).

The reversely quantized value q_(n) is 4.5T when the value of the firstprediction error signal d_(n) is in the range of [3.5T, 5.5T), and 6.5Twhen it is in the range of [5.5T, 7.5T). The reversely quantized valueq_(n) is 9T when the value of the first prediction error signal d_(n) isin the range of [7.5T, 10.5T), and 12T when it is in the range of[10.5T, ∞].

Furthermore, the reversely quantized value q_(n) is −T when the value ofthe first prediction error signal d_(n) is in the range of (−1.5T,0.5T], −2T when it is in the range of (−2.5T, −1.5T], −3T when it is inthe range of (−3.5T, −2.5T], and −4T when it is in the range of (−4.5T,−3.5T].

The reversely quantized value q_(n) is −5.5T when the value of the firstprediction error signal d_(n) is in the range of (−6.5T, −4.5T], and−7.5T when it is in the range of (−8.5T, −6.5T]. The reversely quantizedvalue q_(n) is −10T when the value of the first prediction error signald_(n) is in the range of (−11.5T, −8.5T], and −13T when it is in therange of [∞, −1.5T].

Also in the second embodiment, the quantization step size T_(n) is madelarge when the code L_(n) becomes large, as can be seen from Table 2.That is, the quantization step size is made small when the predictionerror signal d_(n) is small, while being made large when it is large.

Also in the second embodiment, when the prediction error signal d_(n)which is a difference between the input signal x_(n) and the predictingsignal y_(n) is zero, the reversely quantized value q_(n) is zero, as inthe first embodiment. When the prediction error signal d_(n) is zero asin a silent section of a voice signal, therefore, a quantizing error isdecreased.

When the absolute value of the first prediction error signal d_(n) israpidly changed from a large value to a small value, a large valuecorresponding to the previous prediction error signal d_(n) whoseabsolute value is large is maintained as the quantization step size.However, the reversely quantized value q_(n) can be made zero, so thatthe quantizing error is decreased.

In the first embodiment, the quantization step size at each time pointmay, in some case, be changed. When the quantization step size isdetermined at a certain time point, however, the quantization step sizeis constant irrespective of the absolute value of the prediction errorsignal d_(n) at that time point. On the other hand, in the secondembodiment, even in a case where the quantization step size T_(n) isdetermined at a certain time point, the substantial quantization stepsize is decreased when the absolute value of the prediction error signald_(n) is relatively small, while being increased when the absolute valueof the prediction error signal d_(n) is relatively large.

Therefore, the second embodiment has the advantage that the quantizingerror in a case where the absolute value of the prediction error signald_(n) is small can be made smaller, as compared with that in the firstembodiment. When the absolute value of the prediction error signal d_(n)is small, a voice may be small in many cases, so that the quantizingerror greatly affects the degradation of a reproduced voice. If thequantizing error in a case where the prediction error signal d_(n) issmall can be decreased, therefore, this is useful.

On the other hand, when the absolute value of the prediction errorsignal d_(n) is large, a voice may be large in many cases, so that thequantizing error does not greatly affect the degradation of a reproducedvoice. Even if the substantial quantization step size is increased in acase where the absolute value of the prediction error signal d_(n) isrelatively large as in the second embodiment, therefore, there are fewdemerits therefor.

Furthermore, when the absolute value of the prediction error signald_(n) is rapidly changed from a small value to a large value, thequantization step size is small. In the second embodiment, when theabsolute value of the prediction error signal d_(n) is large, however,the substantial quantization step size is made larger than thequantization step size, so that the quantizing error can be decreased.

Although in the first embodiment and the second embodiment, descriptionwas made of a case where the present invention is applied to the ADPCM,the present invention is applicable to APCM in which the input signalx_(n) is used as it is in place of the first prediction error signald_(n) in the ADPCM.

[3] Description of Third Embodiment

Referring now to FIG. 10, a third embodiment of the present inventionwill be described.

FIG. 10 illustrates the schematic construction of an APCM encoder 201and an APCM decoder 202. n used in the following description is aninteger.

Description is now made of the APCM encoder 201.

A signal generator 219 generates a correcting signal a_(n) on the basisof a signal x_(n) inputted to the APCM encoder 201 and a quantizationstep size T_(n) obtained by a first quantization step size updatingdevice 218. That is, the signal generator 219 generates the correctingsignal a_(n) on the basis of the following equation (34):

in the case of x_(n)≧0: a_(n)=T_(n)/2

in the case of x_(n)<0: a_(n)=−T_(n)/2  (34)

A first adder 212 finds a corrected input signal g_(n) on the basis ofthe input signal x_(n) and the correcting signal a_(n) obtained by thesignal generator 219. That is, the first adder 212 finds the correctedinput signal g_(n) on the basis of the following equation (35):

g_(n)=x_(n)+a_(n)  (35)

Consequently, the corrected input signal g_(n) is expressed by thefollowing equation (36):

in the case of d_(n)≧0: g_(n)=x_(n)+T_(n)/2

in the case of d_(n)<0: g_(n)=x_(n)−T_(n)/2  (36)

A first adaptive quantizer 214 codes the corrected input signal g_(n)found by the first adder 212 on the basis of the quantization step sizeT_(n) obtained by the first quantization step size updating device 218,to find a code L_(n). That is, the first adaptive quantizer 214 findsthe code L_(n) on the basis of the following equation (37). The foundcode L_(n) is sent to a memory 203.

L_(n)=[g_(n)/T_(n)]  (37)

In the equation (37), [ ] is Gauss' notation, and represents the maximuminteger which does not exceed a number in the square brackets. Aninitial value of the quantization step size T_(n) is a positive number.

The first quantization step size updating device 218 finds aquantization step size T_(n+1) corresponding to the subsequent voicesignal sampling value x_(n+1) on the basis of the following equation(37). The relationship between the code L_(n) and a function M (L_(n))is as shown in Table 3. Table 3 shows an example in a case where thecode L_(n) is composed of four bits.

T_(n+1)=T_(n)×M(L_(n))  (38)

TABLE 3 L_(n) M (L_(n)) 0 −1 0.8 1 −2 0.8 2 −3 0.8 3 −4 0.8 4 −5 1.2 5−6 1.6 6 −7 2.0 7 −8 2.4

Description is now made of the APCM decoder 202.

A second adaptive reverse quantizer 222 uses a code L_(n)′ obtained fromthe memory 203 and a quantization step size T_(n)′ obtained by a secondquantization step size updating device 223, to find w_(n)′ (a reverselyquantized value) on the basis of the following equation (39) The foundreproducing signal w_(n)′ is outputted from the APCM decoder 202.

w_(n)′=L_(n)′×T_(n)′  (39)

The second quantization step size updating device 223 uses the codeL_(n)′ obtained from the memory 203, to find a quantization step sizeT_(n+1)′ used with respect to the subsequent code L_(n+1)′ on the basisof the following equation (40). The relationship between the code L_(n)′and a function M (L_(n)′) is the same as the relationship between thecode L_(n) and the function M (L_(n)) in Table 3.

T_(n+1)′=T_(n)×M(L_(n)′)  (40)

In the third embodiment, a reproducing signal w_(n)′ obtained byreversely quantizing the code L_(n) corresponding to a section where theabsolute value of the input signal x_(n) is small is approximately zero.

In the above-mentioned third embodiment, the code L_(n) may be found onthe basis of the corrected input signal g_(n) and a table previouslystoring the relationship between the signal g_(n) and the code L_(n),and the quantization step size T_(n+1) corresponding to the subsequentinput signal x_(n+1) may be found on the basis of the found code L_(n)and a table previously storing the relationship between the code L_(n)and the quantization step size T_(n+1) corresponding to the subsequentinput signal x_(n+1).

In this case, the respective tables storing the relationship between thesignal g_(n) and the code L_(n) and the relationship between the codeL_(n) and the quantization step size T_(n+1) corresponding to thesubsequent input signal x_(n+1) are produced so as to satisfy thefollowing conditions (a), (b), and (c):

(a) the quantization step size T_(n) is so changed as to be increasedwhen the absolute value of the input signal x_(n) is so changed as to beincreased.

(b) the reproducing signal w_(n)′ obtained by reversely quantizing thecode L_(n) corresponding to the section where the absolute value of theinput signal x_(n) is small is approximately zero.

(c) the substantial quantization step size corresponding to a sectionwhere the absolute value of the input signal x_(n) is large is larger,as compared with that corresponding to the section where the absolutevalue of the input signal x_(n) is small.

Industrial Applicability

A voice coding method according to the present invention is suitable foruse in voice coding methods such as ADPCM and APCM.

What is claimed is:
 1. A voice coding method comprising: the first stepof adding, when a first prediction error signal d_(n) which is adifference between an input signal x_(n) and a predicted value y_(n)corresponding to the input signal x_(n) is not less than zero, one-halfof a quantization step size T_(n) to the first prediction error signald_(n) to produce a second prediction error signal e_(n), whilesubtracting, when the first prediction error signal d_(n) is less thanzero, one-half of the quantization step size T_(n) from the firstprediction error signal d_(n) to produce a second prediction errorsignal e_(n); the second step of finding a code L_(n) on the basis ofthe second prediction error signal e_(n) found in the first step and thequantization step size T_(n); the third step of finding a reverselyquantized value q_(n) on the basis of the code L_(n) found in the secondstep; the fourth step of finding a quantization step size T_(n+1)corresponding to the subsequent input signal x_(n+1) on the basis of thecode L_(n) found in the second step; and the fifth step of finding apredicted value y_(n+1) corresponding to the subsequent input signalx_(n+1) on the basis of the reversely quantized value q_(n) found in thethird step and the predicted value y_(n).
 2. The voice coding methodaccording to claim 1, wherein in said second step, the code L_(n) isfound on the basis of the following equation: L_(n)=[e_(n)/T_(n)] where[ ] is Gauss' notation, and represents the maximum integer which doesnot exceed a number in the square brackets.
 3. The voice coding methodaccording to claim 1, wherein in said third step, the reverselyquantized value q_(n) is found on the basis of the following equation:g_(n)=L_(n)×T_(n).
 4. The voice coding method according to claim 1,wherein in said fourth step, the quantization step size T_(n+1) is foundon the basis of the following equation: T_(n+1)=T_(n)×M(L_(n)) where M(L_(n)) is a value determined depending on L_(n).
 5. The voice codingmethod according to claim 1, wherein in said fifth step, the predictedvalue y_(n+1) is found on the basis of the following equation:y_(n+1)=y_(n)+q_(n).
 6. A voice coding method comprising: the first stepof adding, when a first prediction error signal d_(n) which is adifference between an input signal x_(n) and a predicted value y_(n)corresponding to the input signal x_(n) is not less than zero, one-halfof a quantization step size T_(n) to the first prediction error signald_(n) to produce a second prediction error signal e_(n), whilesubtracting, when the first prediction error signal d_(n) is less thanzero, one-half of the quantization step size T_(n) from the firstprediction error signal d_(n) to produce a second prediction errorsignal e_(n); the second step of finding, on the basis of the secondprediction error signal e_(n) found in the first step and a tablepreviously storing the relationship between the second prediction errorsignal e_(n) and a code L_(n), the code L_(n); the third step offinding, on the basis of the code L_(n) found in the second step and atable previously storing the relationship between the code L_(n) and areversely quantized value q_(n), the reversely quantized value q_(n);the fourth step of finding, on the basis of the code L_(n) found in thesecond step and a table previously storing the relationship between thecode L_(n) and a quantization step size T_(n+1) corresponding to thesubsequent input signal x_(n+1), the quantization step size T_(n+1)corresponding to the subsequent input signal x_(n+1); and the fifth stepof finding a predicted value y_(n+1) corresponding to the subsequentinput signal x_(n+1) on the basis of the reversely quantized value q_(n)found in the third step and the predicted value y_(n), wherein each ofthe tables being produced so as to satisfy the following conditions (a),(b) and (c): (a) The quantization step size T_(n) is so changed as to beincreased when the absolute value of the difference d_(n) is so changedas to be increased, (b) The reversely quantized value q_(n) of the codeL_(n) corresponding to a section where the absolute value of thedifference d_(n) is small is approximately zero, and (c) A substantialquantization step size corresponding to a section where the absolutevalue of the difference d_(n) is large is larger, as compared with thatcorresponding to the section where the absolute value of the differenced_(n) is small.
 7. The voice coding method according to claim 6, whereinin said fifth step, the predicted value y_(n+1) is found on the basis ofthe following equation: y_(n+1)=y_(n)+q_(n).